Original Research Paper
Analogue Integrated Circuits
S. M. Anisheh; M. Khoshnoud; M. Radmehr
Abstract
Background and Objectives: A Time-to-Digital Converter (TDC) is a fundamental electronic component that converts time intervals into digital representations. It plays a critical role in high-precision applications such as particle physics experiments, time-of-flight measurements, and the processing of ...
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Background and Objectives: A Time-to-Digital Converter (TDC) is a fundamental electronic component that converts time intervals into digital representations. It plays a critical role in high-precision applications such as particle physics experiments, time-of-flight measurements, and the processing of high-frequency signals in communication systems. This paper presents a comprehensive study on the design and simulation of two innovative low-power TDC architectures.Methods: The approach introduces a novel low-power D Flip-Flop (D-FF) circuit using transmission gates (TG) and CMOS inverters to reduce power consumption while maintaining high performance. Specialized low-power delay cells are proposed for Flash TDC implementation. Detailed simulations were conducted using Cadence software with a 0.18 μm CMOS fabrication process at a supply voltage of 1.8 V.Results: The results demonstrate significant improvements in power efficiency and performance metrics, indicating the potential of the proposed TDC designs for future applications requiring precise temporal measurements. The Figure of Merit (FOM) values of the two proposed structures are 0.033 and 0.020, respectively.Conclusion: Power consumption in TDCs is a critical factor, as it directly influences the overall efficiency of electronic systems. Reducing power consumption can lead to decreased energy use, improved thermal management, and an extended lifespan for devices. Conversely, higher power consumption can generate excessive heat, which can negatively impact the system's performance and reliability. Thus, it is vital to strike an optimal balance between accuracy and power consumption in TDCs to enhance the longevity of electronic devices. This paper presents the design of delay cell circuits and a D-FF using a 0.18 µm CMOS process with a 1.8 V supply voltage. The power consumption of the proposed delay cells has been minimized through the application of the body bias technique. The performance of the delay cell has been evaluated in flash TDC circuits, and the results demonstrate the effective performance of the proposed structures.
Original Research Paper
Natural Language Processing
M. J. Nasri-Lowshani; J. Salimi Sartakhti; H. Ebrahimpour-Komole
Abstract
Background and Objectives: Developing efficient task-oriented dialogue systems capable of handling multilingual interactions is a growing area of research in natural language processing (NLP). In this paper, we propose SenSimpleDS, a deep reinforcement learning-based joint task-oriented dialogue system, ...
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Background and Objectives: Developing efficient task-oriented dialogue systems capable of handling multilingual interactions is a growing area of research in natural language processing (NLP). In this paper, we propose SenSimpleDS, a deep reinforcement learning-based joint task-oriented dialogue system, designed for multilingual conversations.Methods: The system utilizes a deep Q-network and the SBERT model to represent the dialogue environment. We introduce two variants, SenSimpleDS+ and SenSimpleDS-NSP, which incorporate modifications in the ε-greedy method and leverage next sequence prediction (NSP) using BERT to refine the reward function. These methods are evaluated on datasets in English, Persian, Spanish, and German, and compared with baseline methods such as SimpleDS and SCGSimpleDS.Results: Our experimental results demonstrate that the proposed methods outperform the baselines in terms of average collected rewards, requiring fewer learning steps to achieve optimal dialogue policies. Notably, the incorporation of NSP significantly improves performance by optimizing reward collection. The multilingual SenSimpleDS further showcases the system’s ability to function across languages using a random forest classifier for language detection and MPNet for environment construction. In addition to system evaluations, we introduce a new Persian dataset for task-oriented dialogue in the restaurant domain, expanding the resources available for developing dialogue systems in low-resource languages.Conclusion: SenSimpleDS, a deep reinforcement learning-based joint task-oriented dialogue system, demonstrates superior performance over baseline methods by leveraging deep Q-networks, SBERT. The integration of next sequence prediction (NSP) significantly enhances reward optimization, enabling faster convergence to optimal dialogue policies. This work establishes a foundation for future research in multilingual dialogue systems, with potential applications across diverse service domains.
Original Research Paper
Power Electronics
S. Ebrahimzadeh; F. Sedaghati; H. Dolati
Abstract
Background and Objectives: To achieve zero carbon emissions, renewable energy sources have gained noteworthy regard due to their dependable performance, cost efficiency, and adaptability within systems. Increasing adoption of renewable energy sources and electric vehicle (EV) has led to a growing need ...
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Background and Objectives: To achieve zero carbon emissions, renewable energy sources have gained noteworthy regard due to their dependable performance, cost efficiency, and adaptability within systems. Increasing adoption of renewable energy sources and electric vehicle (EV) has led to a growing need for enhanced voltage boost capability. Nevertheless, most of DC sources such as solar cells have a restricted capacity for boosting power. Multilevel inverters can operate as interfaces. In this study, two topologies of switched-capacitor multilevel inverters (SC-MLI) is suggested to overcome the mentioned constraints.Methods: Each stage of the introduced SC-MLI comprises a capacitor, a DC voltage supply, a diode, and two power electronic switches. A comprehensive analysis of the operational principles, and the characteristics of the presented converter, including its charging and discharging behaviors, are provided. Furthermore, the phase-disposition pulse width modulation (PD-PWM) technique is employed to generate the output voltage waveform of the introduced multilevel SC inverter.Results: In the recommended topologies, the quantity of semiconductor power switches, isolated DC voltage supply, diodes, and so, volume and cost of the overall system are decreased in compare to similar SC-MLI topologies. The voltage across the capacitors is self-balanced accurately without using any auxiliary circuits or closed-loop systems. To validate the proposed SC-MLI's effective operation, the implemented topology's simulation and measurement results are presented. The total harmonic distortion for the 17-level inverter using the PD-PWM technique at a modulation index 1 obtained 6.97%. Conclusion: Comprehensive comparative analysis reveals that the introduced topologies have merits and superior performance compared to existing solutions regarding component number, voltage boost factor (BF), and voltage stress. Also, simulation and experimental test results verify theoretical analysis.
Original Research Paper
Image Processing
A. Habibi; M. Afrasiabi; M. Chaparian
Abstract
Background and Objectives: Facial recognition technology has become a reliable solution for access control, augmenting traditional biometric methods. It primarily focuses on two core tasks: face verification, which determines whether two images belong to the same individual, and face identification, ...
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Background and Objectives: Facial recognition technology has become a reliable solution for access control, augmenting traditional biometric methods. It primarily focuses on two core tasks: face verification, which determines whether two images belong to the same individual, and face identification, which matches a face to a database. However, facial recognition still faces critical challenges such as variations in pose, illumination, facial expressions, image noise, and limited training samples per subject.Method: This study employs a Siamese network based on the Xception architecture within a transfer learning framework to perform one-shot face verification. The model is trained to compare image pairs rather than classify them individually, using deep feature extraction and Euclidean distance measurement, optimized through a contrastive loss function.Results: The proposed model achieves high verification accuracy on benchmark datasets, reaching 97.6% on the Labeled Faces in the Wild (LFW) dataset and 96.25% on the Olivetti Research Laboratory (ORL) dataset. These results demonstrate the model’s robustness and generalizability across datasets with diverse facial characteristics and limited training data.Conclusion: Our findings indicate that the Siamese-Xception architecture is a robust and effective approach for facial verification, particularly in low-data scenarios. This method offers a practical, scalable solution for real-world facial recognition systems, maintaining high accuracy despite data constraints.
Original Research Paper
Artificial Intelligence
A. Mohammadi; M. R. Pajoohan; A. M. Zareh Bidoki
Abstract
Background and Objectives: In Natural Language Processing (NLP), sentiment analysis is crucial for understanding and extracting aspects and opinions expressed in textual data. Recent methods have emphasized determining polarity in multi-domain sentiment analysis while giving less attention to aspect ...
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Background and Objectives: In Natural Language Processing (NLP), sentiment analysis is crucial for understanding and extracting aspects and opinions expressed in textual data. Recent methods have emphasized determining polarity in multi-domain sentiment analysis while giving less attention to aspect and opinion extraction. Furthermore, the terms that convey aspects and opinions may have different importance in different domains, and this difference should be considered to enhance the extraction of aspect-opinion pairs. Methods: To address these challenges, we propose a Weighted Words Multi-Domain (WWMD) model for aspect-opinion pairs extraction, consisting of a self-attention mechanism and a dense network. The self-attention mechanism extracts each word's importance according to the sentence's overall meaning. The dense network is used for domain prediction. It assigns greater weight to words relevant to each domain, which leads to considering the different significance of terms across various contexts. Adding an attention mechanism to the domain module allows for a clearer understanding of different aspects and opinions across various domains. We utilize a two-channel approach, one channel extracts aspects and opinions, while the other extracts the relationships between them. The weighted words extracted by our model are simultaneously considered as the input for both channels.Results: Using weighted words specific to each domain, improves the model output.Conclusion: Evaluation results on benchmark datasets demonstrate the superiority of the proposed model compared to state-of-the-art techniques.
Original Research Paper
Artificial Intelligence
M. Shahbazi Khojasteh; A. Salimi Badr
Abstract
Background and Objectives: Unmanned Aerial Vehicles (UAVs) face significant challenges in navigating narrow passages within GPS-denied environments due to sensor and computational limitations. While deep reinforcement learning (DRL) has improved navigation, many methods rely on costly sensors like depth ...
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Background and Objectives: Unmanned Aerial Vehicles (UAVs) face significant challenges in navigating narrow passages within GPS-denied environments due to sensor and computational limitations. While deep reinforcement learning (DRL) has improved navigation, many methods rely on costly sensors like depth cameras or LiDAR. This study addresses these issues using a vision-based DRL framework with a monocular camera for autonomous UAV navigation.Methods: We propose a DRL-based navigation system utilizing Proximal Policy Optimization (PPO). The system processes a stack of grayscale monocular images to capture short-term temporal dependencies, approximating the partially observable environment. A custom reward function encourages trajectory optimization by assigning higher rewards for staying near the passage center while penalizing further distances. The navigation system is evaluated in a 3D simulation environment under a GPS-denied scenario.Results: The proposed method achieves a high success rate, surpassing 97% in challenging narrow passages. The system demonstrates superior learning efficiency and robust generalization to new configurations compared to baseline methods. Notably, using stacked frames mitigates computational overhead while maintaining policy effectiveness.Conclusion: Our vision-based DRL approach enables autonomous UAV navigation in GPS-denied environments with reduced sensor requirements, offering a cost-effective and efficient solution. The findings highlight the potential of monocular cameras paired with DRL for real-world UAV applications such as search and rescue and infrastructure inspection. Future work will extend the framework to obstacle avoidance and general trajectory planning in dynamic environments.
Original Research Paper
Wireless Networks
N. Sedighian; A. Karimi; J. Mohammadzadeh; F. Zarafshan
Abstract
Background and Objectives: Vehicular Ad Hoc Networks (VANETs) face significant challenges due to high mobility and rapid topology changes. One of the most critical issues in this context is the clustering process, which directly impacts delay reduction, cluster stability, and overall network efficiency. ...
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Background and Objectives: Vehicular Ad Hoc Networks (VANETs) face significant challenges due to high mobility and rapid topology changes. One of the most critical issues in this context is the clustering process, which directly impacts delay reduction, cluster stability, and overall network efficiency. However, traditional clustering methods such as K-Means and MFO, which mainly rely on simple metrics like distance or signal strength, fail to deliver optimal performance in dynamic environments with variable network density. The primary objective of this study is to design and evaluate an advanced clustering algorithm called AI_MCA (Artificial Intelligence Multi Clustering Algorithm), leveraging artificial intelligence and multi-criteria decision-making. By considering factors such as signal strength, relative speed, node density, and vehicle movement direction, the proposed algorithm forms clusters with higher stability and efficiency in dynamic and high-density environments.Methods: This study uses simulations to evaluate AI_MCA in VANETs, which facilitate vehicle-to-vehicle communication and are characterized by high mobility and rapid position changes.Results: Simulations in NS3 and SUMO show that AI_MCA reduces latency by 20% (12ms vs. 15ms in MFO) and improves cluster stability by 30% (lifetime of 45s vs. 33s in K-Means) within a 600m range. At a 1000m range with 300 nodes, delay increases to 14ms and PDR drops to 88%.Conclusion: AI_MCA outperforms traditional methods like K-Means and MFO, offering a scalable solution for VANET clustering.
Original Research Paper
Software
S. A. H. Eshghazadi; E. Pira; M. Khodizadeh-Nahari; A. Rouhi
Abstract
Background and Objectives: Software testing plays a vital role in software development, aimed at verifying the reliability and stability of software systems. The generation of an effective test suite is key to this process, as it directly impacts the detection of defects and vulnerabilities. However, ...
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Background and Objectives: Software testing plays a vital role in software development, aimed at verifying the reliability and stability of software systems. The generation of an effective test suite is key to this process, as it directly impacts the detection of defects and vulnerabilities. However, for software systems with numerous input parameters, the combinatorial explosion problem hinders the creation of comprehensive test suites. This research introduces a novel approach using the β-Hill Climbing optimizer, an advanced variant of the traditional hill climbing algorithm, to efficiently generate optimal test suites.Methods: The β-Hill Climbing optimizer introduces a dynamic parameter, β, which facilitates a precise balance between exploration and exploitation throughout the search process. To evaluate the performance of this proposed strategy (referred to as BHC), it is compared with TConfig as a mathematical approach, PICT and IPOG as greedy algorithms, and GS, GALP, DPSO, WOA, BAPSO, and GSTG as meta-heuristic methods. These strategies are tested across a variety of configurations to assess their relative efficiency.Results: The reported results confirm that BHC outperforms the others in terms of the size of generated test suites and convergence speed. The statistical analysis of the experimental results on several different configurations shows that BHC outperforms TConfig as a mathematical strategy, PICT and IPOG as greedy strategies, GS, GALP, DPSO, WOA, BAPSO, and GSTG as meta-heuristics by 83%, 88%, 87%, 61%, 61%, 46%, 61%, 62%, and 70%, respectively.Conclusion: The BHC strategy presents a novel and effective approach to optimization, inspired by β-Hill Climbing optimizer for the generation of optimal test suite. Its superior performance in the generation of test suites with smaller size and higher convergence speed compared to other strategies.
